The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
This vignette presents the different utility
functions used internally by deepSTRAPP to cut (mapped)
phylogenies to a given focal-time, and then extract data from
freshly cut tips.
These functions can be used to cut any (mapped) phylogenies outside of a deepSTRAPP run including a regular phylogeny, contMap, densityMap, and BAMM_object with diversification rates.
# ------ Example 1: Cut a regular phylogeny ------ #
# See help of the dedicated function
?deepSTRAPP::cut_phylo_for_focal_time()
## Load eel phylogeny from the R package phytools
# Source: Collar et al., 2014; DOI: 10.1038/ncomms6505
library(phytools)
data(eel.tree)
## Cut phylogeny
# Cut tree to 30 Mya
cut_tree_with_tip_labels <- cut_phylo_for_focal_time(
tree = eel.tree,
focal_time = 30,
keep_tip_labels = TRUE)
## Show tip labels
# Because we used 'keep_tip_labels = TRUE', we kept tip.label on terminal branches
# with a unique descending tip.
# Plot internal node labels on initial tree with cut-off
plot(eel.tree)
abline(v = max(phytools::nodeHeights(eel.tree)[,2]) - 30, col = "red", lty = 2, lwd = 2)
nb_tips <- length(eel.tree$tip.label)
nodelabels_in_cut_tree <- (nb_tips + 1):(nb_tips + eel.tree$Nnode)
nodelabels_in_cut_tree[!(nodelabels_in_cut_tree %in% cut_tree_with_tip_labels$initial_nodes_ID)] <- NA
ape::nodelabels(text = nodelabels_in_cut_tree)
title(main = "Current phylogeny - 0 Mya")
# Plot initial internal node labels on cut tree
plot(cut_tree_with_tip_labels)
ape::nodelabels(text = cut_tree_with_tip_labels$initial_nodes_ID)
title(main = "Past phylogeny - 30 Mya")
# Plot cut tree without keeping tip.label on terminal branches with a unique descending tip.
# All tip.labels are converted to their descending/tipward node ID
cut_tree_without_tip_labels <- cut_phylo_for_focal_time(
tree = eel.tree,
focal_time = 30,
keep_tip_labels = FALSE)
plot(cut_tree_without_tip_labels)
title(main = "Past phylogeny - 30 Mya")
## Show edge labels
# Plot edge labels on initial tree with cut-off
plot(eel.tree)
abline(v = max(phytools::nodeHeights(eel.tree)[,2]) - 30, col = "red", lty = 2, lwd = 2)
edgelabels_in_cut_tree <- 1:nrow(eel.tree$edge)
edgelabels_in_cut_tree[!(1:nrow(eel.tree$edge) %in% cut_tree_with_tip_labels$initial_edges_ID)] <- NA
ape::edgelabels(text = edgelabels_in_cut_tree)
title(main = "Current phylogeny - 0 Mya")
# Plot initial edge labels on cut tree
plot(cut_tree_with_tip_labels)
ape::edgelabels(text = cut_tree_with_tip_labels$initial_edges_ID)
title(main = "Past phylogeny - 30 Mya")
# ------ Example 2: Cut a contMap ------ #
# See help of the dedicated function
?deepSTRAPP::cut_contMap_for_focal_time()
# A contMap is a phylogeny with estimated continuous ancestral trait values mapped along branches.
# It is typically obtained with `[phytools::contMap()]`.
# Within deepSTRAPP, it is part of the output of `[deepSTRAPP::prepare_trait_data()]`
# when used on a continuous trait.
## Load mammals phylogeny and data from the R package motmot (data included in deepSTRAPP)
# Initial data source: Slater, 2013; DOI: 10.1111/2041-210X.12084
data(mammals, package = "deepSTRAPP")
# Get the phylogeny
mammals_tree <- mammals$mammal.phy
# Get the continuous trait data
mammals_data <- setNames(object = mammals$mammal.mass$mean,
nm = row.names(mammals$mammal.mass))[mammals_tree$tip.label]
# Run a stochastic mapping based on a Brownian Motion model
# for Ancestral Trait Estimates to obtain a "contMap" object
mammals_contMap <- phytools::contMap(mammals_tree, x = mammals_data,
res = 100, # Number of time steps
plot = FALSE)
# Set focal time
focal_time <- 80
## Cut contMap to 80 Mya while keeping tip.label
# on terminal branches with a unique descending tip.
updated_contMap <- cut_contMap_for_focal_time(
contMap = mammals_contMap,
focal_time = focal_time,
keep_tip_labels = TRUE)
# Plot node labels on initial stochastic map with cut-off
plot_contMap(mammals_contMap)
ape::nodelabels()
abline(v = max(phytools::nodeHeights(mammals_contMap$tree)[,2]) - focal_time,
col = "red", lty = 2, lwd = 2)
title(main = "Current contMap - 0 Mya")
# Plot initial node labels on cut stochastic map
plot_contMap(updated_contMap)
ape::nodelabels(text = updated_contMap$tree$initial_nodes_ID)
title(main = "Past contMap - 80 Mya")
## Cut contMap to 80 Mya while NOT keeping tip.label.
updated_contMap <- cut_contMap_for_focal_time(
contMap = mammals_contMap,
focal_time = focal_time,
keep_tip_labels = FALSE)
# Plot node labels on initial stochastic map with cut-off
plot_contMap(mammals_contMap)
ape::nodelabels()
abline(v = max(phytools::nodeHeights(mammals_contMap$tree)[,2]) - focal_time,
col = "red", lty = 2, lwd = 2)
title(main = "Current contMap - 0 Mya")
# Plot initial node labels on cut stochastic map
plot_contMap(updated_contMap)
ape::nodelabels(text = updated_contMap$tree$initial_nodes_ID)
title(main = "Past contMap - 80 Mya")
# ------ Example 3: Cut densityMaps ------ #
# See help of the dedicated function
?deepSTRAPP::cut_densityMaps_for_focal_time()
# A densityMap is a phylogeny with posterior probabilities of a given state mapped along branches.
# It is typically obtained with `[phytools::densityMap()]`.
# Within deepSTRAPP, it is part of the output of `[deepSTRAPP::prepare_trait_data()]`
# when used on a categorical trait.
# deepSTRAPP also offers to overlay densityMaps of all states to obtain a single mapping of all states
# on a unique phylogeny with `[deepSTRAPP::plot_densityMaps_overlay()]`
#### 1/ Prepare data ####
## Load mammals phylogeny and data from the R package motmot (data included in deepSTRAPP)
# Initial data source: Slater, 2013; DOI: 10.1111/2041-210X.12084
data(mammals, package = "deepSTRAPP")
# Get the phylogeny
mammals_tree <- mammals$mammal.phy
# Get the continuous trait data
mammals_data <- setNames(object = mammals$mammal.mass$mean,
nm = row.names(mammals$mammal.mass))[mammals_tree$tip.label]
# Convert mass data into categories
mammals_mass <- setNames(object = mammals$mammal.mass$mean,
nm = row.names(mammals$mammal.mass))[mammals_tree$tip.label]
mammals_data <- mammals_mass
mammals_data[seq_along(mammals_data)] <- "small"
mammals_data[mammals_mass > 5] <- "medium"
mammals_data[mammals_mass > 10] <- "large"
table(mammals_data)
## Select color scheme for states
colors_per_states <- c("darkblue", "dodgerblue", "lightblue")
names(colors_per_states) <- c("small", "medium", "large")
# Produce densityMaps using stochastic character mapping based on an equal-rates (ER) Mk model
mammals_cat_data <- prepare_trait_data(tip_data = mammals_data, phylo = mammals_tree,
trait_data_type = "categorical",
evolutionary_models = "ER",
nb_simulations = 100,
colors_per_levels = colors_per_states)
# Set focal time
focal_time <- 80
#### 2/ Plot a unique cut densityMap ####
# Extract the density map for small mammals (state 3 = "small")
mammals_densityMap_small <- mammals_cat_data$densityMaps[[3]]
## Cut densityMap to 80 Mya while keeping tip.label
# on terminal branches with a unique descending tip.
updated_mammals_densityMap_small <- cut_densityMap_for_focal_time(
densityMap = mammals_densityMap_small,
focal_time = focal_time,
keep_tip_labels = TRUE)
# Plot node labels on initial stochastic map with cut-off
phytools::plot.densityMap(mammals_densityMap_small)
ape::nodelabels()
abline(v = max(phytools::nodeHeights(mammals_densityMap_small$tree)[,2]) - focal_time,
col = "red", lty = 2, lwd = 2)
title(main = "Current densityMap - 0 Mya")
# Plot initial node labels on cut stochastic map
phytools::plot.densityMap(updated_mammals_densityMap_small)
ape::nodelabels(text = updated_mammals_densityMap_small$tree$initial_nodes_ID)
title(main = "Past densityMap - 80 Mya")#> mammals_data
#> large medium small
#> 36 83 92
#> make.simmap is sampling character histories conditioned on
#> the transition matrix
#>
#> Q =
#> large medium small
#> large -0.006199435 0.003099718 0.003099718
#> medium 0.003099718 -0.006199435 0.003099718
#> small 0.003099718 0.003099718 -0.006199435
#> (specified by the user);
#> and (mean) root node prior probabilities
#> pi =
#> large medium small
#> 0.3333333 0.3333333 0.3333333
#### 3/ Plot set of overlaid densityMaps ####
## Cut all densityMaps to 80 Mya while keeping tip.label
# on terminal branches with a unique descending tip.
updated_mammals_densityMaps <- cut_densityMaps_for_focal_time(
densityMaps = mammals_cat_data$densityMaps,
focal_time = focal_time,
keep_tip_labels = TRUE)
# Plot node labels on initial stochastic map with cut-off
plot_densityMaps_overlay(densityMaps = mammals_cat_data$densityMaps,
colors_per_levels = colors_per_states)
ape::nodelabels()
abline(v = max(phytools::nodeHeights(mammals_cat_data$densityMaps[[1]]$tree)[,2]) - focal_time,
col = "red", lty = 2, lwd = 2)
title(main = "Current overlaid densityMaps - 0 Mya")
# Plot initial node labels on cut stochastic map
plot_densityMaps_overlay(densityMaps = updated_mammals_densityMaps,
colors_per_levels = colors_per_states)
ape::nodelabels(text = updated_mammals_densityMaps[[1]]$tree$initial_nodes_ID)
title(main = "Past overlaid densityMaps - 80 Mya")
# ------ Example 3: Cut a BAMM_object ------ #
# See help of the dedicated function
?deepSTRAPP::update_rates_and_regimes_for_focal_time()
# A BAMM_object is the typical output of a Bayesian Analysis of Macroevolutionary Mixtures (BAMM).
# It is a phylogeny with additional elements describing diversification dynamics,
# including estimates of diversification rates over BAMM posterior samples.
# Within deepSTRAPP, it is the output of `[deepSTRAPP::prepare_diversification_data()]`.
# Similarly to phylogenies, contMap, and densityMaps, it can be updated for a given focal-time
# by cutting branches at focal-time and updating tip rates.
## Load the BAMM_object summarizing 1000 posterior samples of BAMM
data(whale_BAMM_object, package = "deepSTRAPP")
# Set focal-time to 5 My
focal_time = 5
## Update the BAMM object
whale_BAMM_object_5My <- update_rates_and_regimes_for_focal_time(
BAMM_object = whale_BAMM_object,
focal_time = 5,
update_all_elements = TRUE,
keep_tip_labels = TRUE)
## Explore updated outputs
# Extract speciation rates for t = 0My
speciation_tip_rates_0My <- whale_BAMM_object$meanTipLambda
names(speciation_tip_rates_0My) <- whale_BAMM_object$tip.label
speciation_tip_rates_0My
# Extract speciation rates for t = 5My
speciation_tip_rates_5My <- whale_BAMM_object_5My$meanTipLambda
names(speciation_tip_rates_5My) <- whale_BAMM_object_5My$tip.label
speciation_tip_rates_5My
# Speciation rates have been updated so that they reflect values estimated at the focal-time (t = 5My).
# Extract extinction rates for t = 0My
extinction_tip_rates_0My <- whale_BAMM_object$meanTipMu
names(extinction_tip_rates_0My) <- whale_BAMM_object$tip.label
extinction_tip_rates_0My
# Extract extinction rates for t = 5My
extinction_tip_rates_5My <- whale_BAMM_object_5My$meanTipMu
names(extinction_tip_rates_5My) <- whale_BAMM_object_5My$tip.label
extinction_tip_rates_5My
# Extinction rates have been updated so that they reflect values estimated at the focal-time (t = 5My).
## Plot BAMM_object
# Add "phylo" class to be compatible with phytools::nodeHeights()
class(whale_BAMM_object) <- unique(c(class(whale_BAMM_object), "phylo"))
root_age <- max(phytools::nodeHeights(whale_BAMM_object)[,2])
# Remove temporary "phylo" class
class(whale_BAMM_object) <- setdiff(class(whale_BAMM_object), "phylo")
# Plot initial BAMM_object for t = 0 My
plot_BAMM_rates(whale_BAMM_object, add_regime_shifts = TRUE,
labels = TRUE, legend = TRUE,
par.reset = FALSE) # Keep plotting parameters in memory to use abline().
abline(v = root_age - focal_time,
col = "red", lty = 2, lwd = 2)
title(main = "\nPresent BAMM rates - 0 Mya")
# Plot updated BAMM_object for t = 5 My
plot_BAMM_rates(whale_BAMM_object_5My, add_regime_shifts = TRUE,
labels = TRUE, legend = TRUE)
title(main = "\nPast BAMM rates - 5 Mya")
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.